Risk‐Based Decision Support Tools: Protecting Rail‐Centered Transit Corridors from Cascading Effects

We consider the value of decision support tools for passenger rail system managers. First, we call for models that follow events along main rail lines and then into the surrounding environment where they can cascade onto connected light rail, bus, auto, truck, and other transport modes. Second, we suggest that both probabilistic risk assessment (PRA‐based) and agent‐based models have a role to play at different scales of analysis and for different kinds of risks. Third, we argue that economic impact tools need more systematic evaluation. Fourth, we note that developers of decision support tools face a challenge of balancing their desire for theoretical elegance and the tendency to focus only on high consequence events against decisionmakers’ mistrust of complex tools that they and their staff cannot manage and incorporate into their routine operations, as well as the high costs of developing, updating, and applying decision support tools to transport systems undergoing budget cuts and worker and service reductions.     

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically‐oriented models to advanced physical‐flow‐based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large‐scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.     

An interactive city planning model

Increased man-machine communications capability can provide improvements in problem solving. This improvement can be achieved by designing flexibility of input and output through the use of interactive graphics. This paper outlines an interactive simulation system for use in city planning. In this system, simulation models of a city's demography, employment, transportation, utilities, and communications systems are interfaced with each other and with a graphics screen through the use of a module called the interface control program. This program functions as a system monitor and recognizes what, where, and when information should be transferred through the system. The interactive city planning model allows applications-oriented problem solvers to effectively use the problem solving capability of the computer to respond to events as they develop during a simulation run.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions

Critical infrastructures provide society with services essential to its functioning, and extensive disruptions give rise to large societal consequences. Risk and vulnerability analyses of critical infrastructures generally focus narrowly on the infrastructure of interest and describe the consequences as nonsupplied commodities or the cost of unsupplied commodities; they rarely holistically consider the larger impact with respect to higher‐order consequences for the society. From a societal perspective, this narrow focus may lead to severe underestimation of the negative effects of infrastructure disruptions. To explore this theory, an integrated modeling approach, combining models of critical infrastructures and economic input–output models, is proposed and applied in a case study. In the case study, a representative model of the Swedish power transmission system and a regionalized economic input–output model are utilized. This enables exploration of how a narrow infrastructure or a more holistic societal consequence perspective affects vulnerability‐related mitigation decisions regarding critical infrastructures. Two decision contexts related to prioritization of different vulnerability‐reducing measures are considered—identifying critical components and adding system components to increase robustness. It is concluded that higher‐order societal consequences due to power supply disruptions can be up to twice as large as first‐order consequences, which in turn has a significant effect on the identification of which critical components are to be protected or strengthened and a smaller effect on the ranking of improvement measures in terms of adding system components to increase system redundancy.     

Production planning in practice

The changes and pressures facing the manufacturing and engineering industries today are increasing the importance of effective aggregate manpower and production planning. Several different theoretical optimisation models to tackle this problem have been described in detail in the literature but there have been few applications of them in practice.The reasons for this are many but include: the difficulty in expressing managements' conflicting and mixed objectives in an objective function; the necessity to oversimplify real life systems to enable these methods to be used; the simplistic approach to manpower planning used in these models; the difficulty in gaining managements' acceptance and finally the fact that what management actually wants is a tool to assist them in planning and decision making.What is being used by many managements is a case-study deterministic simulation model. Many companies are adopting this type of model for all types of planning and twelve out of twenty-seven companies visited in a research project described in this paper were using this type of model for aggregate manpower and production planning. It is proving to be an effective management tool and is being readily accepted principally because modern specialised financial modelling languages are enabling these models to be built, understood and used by non-specialist managers.     

Hazardous Materials Releases in the Northridge Earthquake: Implications for Seismic Risk Assessment

In recent years, increasing attention has been given to the problem of earthquake-initiated hazardous materials releases (EIHRs). While the evidence indicates that EHIRs are an important part of the earthquake hazard profile, little attention has been given to documenting them in a way that could form the basis for more systematic estimates of their probability and consequences. Data from the January 17, 1994 Northridge earthquake document the range and nature of hazardous materials events in the impact area, including those at fixed-site facilities and in transportation systems. Proportional estimates of hazmat incidence are provided, and the implications of these data for developing risk assessments and earthquake hazard management are discussed.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what‐if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia‐New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi‐quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi‐quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.     

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out‐of‐sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.     

Hazard Experience,Geophysical Vulnerability,and Flood Risk Perceptions in a Postdisaster City,the Case of New Orleans

This article investigates the determinants of flood risk perceptions in New Orleans, Louisiana (United States), a deltaic coastal city highly vulnerable to seasonal nuisance flooding and hurricane‐induced deluges and storm surges. Few studies have investigated the influence of hazard experience, geophysical vulnerability (hazard proximity), and risk perceptions in cities undergoing postdisaster recovery and rebuilding. We use ordinal logistic regression techniques to analyze experiential, geophysical, and sociodemographic variables derived from a survey of 384 residents in seven neighborhoods. We find that residents living in neighborhoods that flooded during Hurricane Katrina exhibit higher levels of perceived risk than those residents living in neighborhoods that did not flood. In addition, findings suggest that flood risk perception is positively associated with female gender, lower income, and direct flood experiences. In conclusion, we discuss the implications of these findings for theoretical and empirical research on environmental risk, flood risk communication strategies, and flood hazards planning.     

A Framework for Estimating the Adverse Health Effects of Contamination Events in Water Distribution Systems and its Application

Intentional or accidental releases of contaminants into a water distribution system (WDS) have the potential to cause significant adverse health effects among individuals consuming water from the system. A flexible analysis framework is presented here for estimating the magnitude of such potential effects and is applied using network models for 12 actual WDSs of varying sizes. Upper bounds are developed for the magnitude of adverse effects of contamination events in WDSs and evaluated using results from the 12 systems. These bounds can be applied in cases in which little system‐specific information is available. The combination of a detailed, network‐specific approach and a bounding approach allows consequence assessments to be performed for systems for which varying amounts of information are available and addresses important needs of individual utilities as well as regional or national assessments. The approach used in the analysis framework allows contaminant injections at any or all network nodes and uses models that (1) account for contaminant transport in the systems, including contaminant decay, and (2) provide estimates of ingested contaminant doses for the exposed population. The approach can be easily modified as better transport or exposure models become available. The methods presented here provide the ability to quantify or bound potential adverse effects of contamination events for a wide variety of possible contaminants and WDSs, including systems without a network model.     

Agent‐Based Simulation for Human‐Induced Hazard Analysis

Terrorism could be treated as a hazard for design purposes. For instance, the terrorist hazard could be analyzed in a manner similar to the way that seismic hazard is handled. No matter how terrorism is dealt with in the design of systems, the need for predictions of the frequency and magnitude of the hazard will be required. And, if the human‐induced hazard is to be designed for in a manner analogous to natural hazards, then the predictions should be probabilistic in nature. The model described in this article is a prototype model that used agent‐based modeling (ABM) to analyze terrorist attacks. The basic approach in this article of using ABM to model human‐induced hazards has been preliminarily validated in the sense that the attack magnitudes seem to be power‐law distributed and attacks occur mostly in regions where high levels of wealth pass through, such as transit routes and markets. The model developed in this study indicates that ABM is a viable approach to modeling socioeconomic‐based infrastructure systems for engineering design to deal with human‐induced hazards.     

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.     

Assessment of aircraft logistics planning models

An assessment of military logistics planning models offers a great deal of information about the modelling state of art. Such tools include both analytic and simulation model types. They can deal with both static and dynamic characteristics of the environment. They require highly detailed data for their operation and can compute over a large number of interacting variables. Unfortunately, these models do not satisfy adequately the requirements of the particular logistics issue for which they were assessed, which is whether such models can be used in early logistics planning for new weapon systems. One difficulty is that such planning must make extensive use of tradeoffs and sensitivity analysis to take account of the flexibility and uncertainties existing at the early stages. Another is that the existent models call for detailed data which is usually not available at that time. Therefore, although the models do fulfill a particular useful planning function, they must be replaced or augmented by a new class of models that will much more closely satisfy the planning need. This new capability requires a serious research effort that will benefit not only the military logistics planners, but also other planners dealing with large capital development programs.     

On Funtowicz and Ravetz's “Decision Stake—System Uncertainties” Structure and Recently Developed Risk Perspectives

The Funtowicz and Ravetz model for classifying problem‐solving strategies into applied sciences, professional consultancy, and postnormal sciences is well known in the social science risk literature. The model is illustrated in a diagram based on the two axes: (i) decision stakes—the value dimension (costs, benefits) and (ii) the system uncertainties—the knowledge dimension. These axes resemble the same two dimensions that characterize some recently developed risk perspectives: (a) consequences and the severity of these consequences and (b) associated uncertainties. In this article, we make a detailed comparison of these two types of risk frameworks. We point to similarities and differences in motivation and use. A main conclusion of the article is that these risk perspectives all provide adequate scientific bases for the Funtowicz and Ravetz model. New insights are provided on the understanding of what the outcome stakes/consequences and uncertainty dimensions really capture in these perspectives and frameworks.     

Spatial Risk Assessment Across Large Landscapes with Varied Land Use: Lessons from a Conservation Assessment of Military Lands

Spatial decision-support tools are necessary for assessment and management of threats to biodiversity, which in turn is necessary for biodiversity conservation. In conjunction with the U.S. Geological Survey-Biological Resources Division's Species at Risk program, we developed a GIS-based spatial decision-support tool for relative risk assessments of threats to biodiversity on the U.S. Army's White Sands Missile Range and Fort Bliss (New Mexico and Texas) due to land uses associated with military missions of the two bases. The project tested use of spatial habitat models, land-use scenarios, and species-specific impacts to produce an assessment of relative risks for use in conservation planning on the 1.2 million-hectare study region. Our procedure allows spatially explicit analyses of risks to multiple species from multiple sources by identifying a set of hazards faced by all species of interest, identifying a set of feasible management alternatives, assigning scores to each species for each hazard, and mapping the distribution of these hazard scores across the region of interest for each combination of species/management alternatives. We illustrate the procedure with examples. We demonstrate that our risk-based approach to conservation planning can provide resource managers with a useful tool for spatial assessment of threats to species of concern.     

Risk Perception,Experience, and Objective Risk: A Cross‐National Study with European Emergency Survivors

Understanding public risk perceptions and their underlying processes is important in order to learn more about the way people interpret and respond to hazardous emergency events. Direct experience with an involuntary hazard has been found to heighten the perceived risk of experiencing the same hazard and its consequences in the future, but it remains unclear if cross‐over effects are possible (i.e., experience with one hazard influencing perceived risk for other hazards also). Furthermore, the impact of objective risk and country of residence on perceived risk is not well understood. As part of the BeSeCu (Behavior, Security, and Culture) Project, a sample of 1,045 survivors of emergencies from seven European countries (i.e., Germany, the Czech Republic, Poland, Sweden, Spain, Turkey, and Italy) was drawn. Results revealed heightened perceived risk for emergency events (i.e., domestic and public fires, earthquakes, floods, and terrorist attacks) when the event had been experienced previously plus some evidence of cross‐over effects, although these effects were not so strong. The largest country differences in perceived risk were observed for earthquakes, but this effect was significantly reduced by taking into account the objective earthquake risk. For fires, floods, terrorist attacks, and traffic accidents, only small country differences in perceived risk were found. Further studies including a larger number of countries are welcomed.     

Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards

The U.S. federal government regulates the reliability of bulk power systems, while the reliability of power distribution systems is regulated at a state level. In this article, we review the history of regulating electric service reliability and study the existing reliability metrics, indices, and standards for power transmission and distribution networks. We assess the foundations of the reliability standards and metrics, discuss how they are applied to outages caused by large exogenous disturbances such as natural disasters, and investigate whether the standards adequately internalize the impacts of these events. Our reflections shed light on how existing standards conceptualize reliability, question the basis for treating large‐scale hazard‐induced outages differently from normal daily outages, and discuss whether this conceptualization maps well onto customer expectations. We show that the risk indices for transmission systems used in regulating power system reliability do not adequately capture the risks that transmission systems are prone to, particularly when it comes to low‐probability high‐impact events. We also point out several shortcomings associated with the way in which regulators require utilities to calculate and report distribution system reliability indices. We offer several recommendations for improving the conceptualization of reliability metrics and standards. We conclude that while the approaches taken in reliability standards have made considerable advances in enhancing the reliability of power systems and may be logical from a utility perspective during normal operation, existing standards do not provide a sufficient incentive structure for the utilities to adequately ensure high levels of reliability for end‐users, particularly during large‐scale events.